Background Pneumoconiosis is the most common, hazardous, and extensive occupational disease in China, and the prevalence in Jiangsu Province is severe.
Objective The study aims to establish a combined model for pneumoconiosis prediction based on the combinaton of ARIMA with grey model GM (1, 1) or generalized neural regression network model (GRNN).
Methods The newly diagnosed cases of pneumoconiosis in Jiangsu Province from January 2006 to December 2017 were used to construct ARIMA model, and the data from January to August 2018 were used as the test values of the model. The residual values generated during the ftng of the ARIMA model were further fitted with GM (1, 1) model after adding a threshold of 3, and then the ARIMA-GM combined model was constructed to ft and predict pneumoconiosis incidence. The fted value of the ARIMA model was taken as the input value of GRNN model and the real value of pneumoconiosis was taken as the output value, and then the ARIMA-GRNN combined model was constructed to ft and predict pneumoconiosis incidence. Mean square error (MSE), mean absolute error (MAE), and mean relatve error (MRE) were used to evaluate the ftng effect.
Results The MSEs of the three models fitting the new cases of pneumoconiosis in Jiangsu Province were ARIMA-GRNN (0.321 4) < ARIMA-GM (0.704 6) < ARIMA (0.807 9), the MAEs were ARIMA-GRNN (0.398 6) < ARIMA-GM (0.632 4) < ARIMA (0.659 1), and the MREs were ARIMA-GRNN (0.161 2) < ARIMA-GM (0.183 8) < ARIMA (0.187 9), respectvely. The MSEs of the three models predictng the pneumoconiosis incidences in Jiangsu Province from January to August 2018 were ARIMA-GRNN (0.084 3) < ARIMA (0.243 5) < ARIMA-GM (0.263 4), the MAEs were ARIMA-GRNN (0.234 5) < ARIMA (0.388 7) < ARIMA-GM (0.416 1), and the MREs were ARIMA-GRNN (0.098 1) < ARIMA (0.108 6) < ARIMA-GM (0.114 9), respectvely. All indicators showed that ARIMA-GRNN combined model had the smallest ftng and predicton error.
Conclusion ARIMA-GRNN model is superior to ARIMA-GM model and ARIMA model in predictng pneumoconiosis incidences in Jiangsu Province.
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